Doctor of Philosophy / Genetics Interdepartmental Program - Plant Pathology / Jesse A. Poland / A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals. The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS). To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models. Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.

Doctor of Philosophy / Department of Agronomy / V. P. Vara Prasad / Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.

With growing concerns about global food supply and environmental impacts of modern agriculture, we are seeing an increased demand for more horticultural research. While research into plant genetics has seen an increased throughput from recent technological advancements, plant phenotypic research throughput has lagged behind. Improvements in open-source image processing software and image capture hardware have created an opportunity for the development of more competitively-priced, faster data-acquisition tools. These tools could be used to collect measurements of plants' phenotype on a much larger scale without sacrificing data quality. This paper demonstrates the feasibility of creating such a tool. The resulting design utilized stereo vision and image processes in the OpenCV project to measure a representative collection of observable plant traits like leaflet length or plant height. After the stereo camera was assembled and calibrated, visual and stereo images of potato plant canopies and tubers(potatoes) were collected. By processing the visual data, the meaningful regions of the image (the canopy, the leaflets, and the tubers) were identified. The same regions in the stereo images were used to determine plant physical geometry, from which the desired plant measurements were extracted. Using this approach, the tool had an average accuracy of 0.15 inches with respect to distance measurements. Additionally, the tool detected vegetation, tubers, and leaves with average Dice indices of 0.98, 0.84, and 0.75 respectively. To compare the tool's utility to that of traditional implements, a study was conducted on a population of 27 potato plants belonging to 9 separate genotypes. Both newly developed and traditional measurement techniques were used to collect measurements of a variety of the plants' characteristics. A multiple linear regression of the plant characteristics on the plants' genetic data showed that the measurements collected by hand were generally better correlated with genetic characteristics than those collected using the developed tool; the average adjusted coefficient of determination for hand-measurements was 0.77, while that of the tool-measurements was 0.66. Though the aggregation of this platform's results is unsatisfactory, this work has demonstrated that such an alternative to traditional data-collection tools is certainly attainable. / Master of Science

<div><p>Wheat is among the most important cereal crops in the world today with respect to the area harvested (219 million ha), production (772 million tonnes), and productivity (3.53 tons/ha). However, global wheat production goals for the coming decades are falling short of needed increases. Among the leading factors hindering yields is abiotic stress which is present in nearly 38% of wheat acres globally. Nevertheless, many standard wheat breeding programs focus on yield and yield related traits (i.e. grain yield, plant height, and test weight) in ideal environments rather than evaluating traits that could lead to enhanced abiotic stress tolerance. In this thesis, we explore the use of root and high-throughput phenotyping strategies to aid in further development of abiotic stress tolerant varieties. </p><p>In the first three experiments, root phenotypes were evaluated in two nitrogen (N) treatments. Over a series of seedling, adult, and multiple-growth-stage destructive plant biomass measurements, above-ground and below-ground traits were analyzed in seven geographically diverse wheat accessions. Root and shoot biomass allocation in fourteen-day-old seedlings were analyzed using paper-roll-supported hydroponic culture in two Hoagland solutions containing 0.5 (low) and 4.0 (high) mM of N. Root traits were digitized using a WINRhizo platform. For biomass analysis at maturity, plants were grown in 7.5-liter pots filled with soil mix using the same concentrations of N. Traits were measured as plants reached maturity. In the third N experiment, above- and below-ground traits were measured at four-leaf stage, stem elongation, heading, post-anthesis, and maturity. At maturity, there was a ~15-fold difference between lines with the largest and smallest root dry matter. However, only ~5-fold difference was observed between genotypes for above-ground biomass. In the third experiment, root growth did not significantly change from stem elongation to maturity. </p><p>In the final experiment, two of these lines were selected for further evaluation under well-watered and drought treatments. This experiment was implemented in a completely randomized design in the Controlled Environment Phenotyping Facility (CEPF) at Purdue University. The differential water treatments were imposed at stem elongation and continued until post-anthesis, when all plants were destructively phenotyped. Image-based height and side-projected area were associated with height and shoot dry matter with correlations of r=1 and r=0.98, respectively. Additionally, 81% of the variation in tiller number was explained using convex hull and side-projected area. Image-based phenotypes were used to model crop growth temporally, through which one of the lines was identified as being relatively more drought tolerant. Finally, the use of the Munsell Color System was explored to investigate drought response.</p><p>These experiments illustrate the value of phenotyping and the use of novel phenotyping strategies in wheat breeding to increase adaptation and development of lines with enhanced abiotic tolerance.</p></div><br>

Master of Science / Department of Computer Science / Mitchell L. Neilsen / Kansas State University is a world leader in the study of small grain genetics to develop new varieties which tolerate a wide range of environmental conditions. A phenotype is a composite of a plants observable traits. Several mobile applications, called PhenoApps, have been developed for field-based, high-throughput phenotyping (HTP) to advance plant breeding programs around the world. These applications require novel image analysis algorithms to be developed to model and extract plant phenotypes. Some of the first algorithms developed were focused on using static image analysis to count and characterize a wide variety of seeds in a single image with a static colored background. This thesis describes both a static algorithm and development of a hopper system for a dynamic, real-time algorithm to accurately count and characterize seeds using a modest mobile device. The static algorithm analyzes a single image of a particular seed sample, captured on a mobile device; whereas, the dynamic algorithm analyzes multiple frames from the video input of a mobile device in real time. Novel 3D models are designed and printed to set a steady flow rate for the seeds, but the analysis is also completed to consider seeds flowing at variable rates and to determine the range of allowable flow rates and achievable precision for a wide variety of seeds. Both algorithms have been implemented in user-friendly mobile applications for realistic, field-based use. A plant breeder can use the applications to both count and characterize a smaller sample using the static approach or a larger sample using the dynamic approach, with seeds sampled in real time without the need to analyze multiple static images. There are many directions for future research to enhance the algorithms performance and accuracy.

Salt stress severely constrains plant performance and global agricultural productivity. 5% of arable land, 20% of irrigated areas and 98% of water reserves worldwide are saline. Improving the salt tolerance of major crop species could help attenuate yield losses and expand irrigation opportunities and provide in situ relief in areas where poverty, food and water scarcity are prevalent. Increasing the salt tolerance of crops with high commercial and nutritional value, such as tomato (Solanum lycopersicum L.), would provide particularly significant economic and health benefits. However, salt tolerance is a complex trait with a limited genetic repertoire in domesticated crop varieties, including tomato, frustrating attempts to breed and engineer tolerant crop varieties. Here, a genome-wide association study (GWAS) was undertaken, leveraging the rich genetic diversity of the wild, salt tolerant tomato Solanum pimpinellifolium and the latest phenotyping technologies to identify traits that contribute to salt tolerance and the genetic basis for variation in those traits. A panel of 220 S. pimpinellifolium accessions was phenotyped, focusing on image-based high-throughput phenotyping over time in controlled and field conditions in young and mature plants. Results reveal substantial natural variation in salt tolerance over time across many traits. In particular, the use of unmanned aerial vehicle (UAV)-based remote sensing in the field allowed high-resolution RGB, thermal and hyperspectral mapping that offers new insights into plant performance in the field, over time. To empower our GWAS and facilitate the identification of candidate genes, a new S. pimpinellifolium reference genome was generated, 811Mb in size, N50 of ~76kb, containing 25,970 annotated genes. Analysis of this reference genome highlighted potential contributors to salt tolerance, including enrichments in genes with stress response functions and a high copy number of the salt tolerance-associated gene inositol- 3-phosphate synthase (I3PS). A recently completed full genome re-sequencing of the panel, along with a newly available pseudomolecule-level assembly of the S. pimpinellifolium genome with N50 of ~11Mb, will serve to drive a GWAS to identify loci associated with traits that contribute to salt tolerance. Further research including gene validation, breeding, genetic modification and gene editing experiments will drive the development of new salt tolerant tomato cultivars.

Master of Science / Department of Agronomy / William T. Schapaugh / Remote sensing technologies might serve as indirect selection tools to improve phenotyping to differentiate genotypes for yield in soybean breeding program as well as the assessment of soybean cyst nematode (SCN), Heterodera glycines. The objective of these studies were to: i) investigate potential use of spectral reflectance indices (SRIs) and canopy temperature (CT) as screening tools for soybean grain yield in an elite, segregating population; ii) determine the most appropriate growth stage(s) to measure SRI’s for predicting grain yield; and iii) estimate SCN population density among and within soybean cultivars utilizing canopy spectral reflectance and canopy temperature. Experiment 1 was conducted at four environments (three irrigated and one rain-fed) in Manhattan, KS in 2012 and 2013. Each environment evaluated 48 F4- derived lines. In experiment 2, two SCN resistant cultivars and two susceptible cultivars were grown in three SCN infested field in Northeast KS, in 2012 and 2013. Initial (Pi) and final SCN soil population (Pf) densities were obtained. Analyses of covariance (ANCOVA) revealed that the green normalized vegetation index (GNDVI) was the best predictive index for yield compared to other SRI’s and differentiated genotype performance across a range of reproductive growth stages. CT did not differentiate genotypes across environments. In experiment 2, relationships between GNDVI, reflectance at single wavelengths (675 and 810 nm) and CT with Pf were not consistent across cultivars or environments. Sudden death syndrome (SDS) may have confounded the relationships between remote sensing data and Pf. Therefore, it would be difficult to assess SCN populations using remote sensing based on these results.

Master of Science / Department of Computing and Information Sciences / Mitchell L. Neilsen / This research is a part of BREAD PHENO, a PhenoApps BREAD project at K-State which combines contemporary advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. In this platform, novel image analysis segmentation algorithms are being developed to model and extract plant phenotypes. As a part of this research, the traditional Watershed segmentation algorithm has been extended and the primary goal is to accurately count and characterize the seeds in an image. The new approach can be used to characterize a wide variety of crops. Further, this algorithm is migrated into Android making use of the Android APIs and the first ever user-friendly Android application implementing the extended Watershed algorithm has been developed for Mobile field-based high-throughput phenotyping (HTP).

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G, barbaderise L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered pm conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination (r(gij) = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

Increasing salinity is a worldwide problem, but the knowledge on how salt enters the roots of plants remains largely unknown. Non-selective cation channels (NSCCs) have been suggested to be the major pathway for the entry of sodium ions (Na+) in several species. The hypothesis tested in this research is that PQ loop (PQL) proteins could form NSCCs, mediate some of the Na+ influx into the root and contribute to ion accumulation and the inhibition of growth in saline conditions. This is based on previous preliminary evidence indicating similarities in the properties of NSCC currents and currents mediated by PQL proteins, such as the inhibition of an inward cation current mediated by PQL proteins by high external calcium and pH acidification. PQL family members belonging to clade one in Arabidopsis and barley were characterized using a reverse genetics approach, electrophysiology and high-throughput phenotyping. Expression of AtPQL1a and HvPQL1 in HEK293 cells increased Na+ and K+ inward currents in whole cell membranes. However, when GFP-tagged PQL proteins were transiently overexpressed in tobacco leaf cells, the proteins appeared to localize to intracellular membrane structures. Based on q-RT-PCR, the levels of mRNA of AtPQL1a, AtPQL1b and AtPQL1c is higher in salt stressed plants compared to control plants in the shoot tissue, while the mRNA levels in the root tissue did not change in response to stress. Salt stress responses of lines with altered expression of AtPQL1a, AtPQL1b and AtPQL1c were examined using RGB and chlorophyll fluorescence imaging of plants growing in soil in a controlled environment chamber. Decreases in the levels of expression of AtPQL1a, AtPQL1b and AtPQL1c resulted in larger rosettes, when measured seven days after salt stress imposition. Interestingly, overexpression of AtPQL1a also resulted in plants having larger rosettes in salt stress conditions. Differences between the mutants and the wild-type plants were not observed at earlier stages, suggesting that PQLs might be involved in long-term responses to salt stress. These results contribute towards a better understanding of the role of PQLs in salinity tolerance and provide new targets for crop improvement.